Digital reading applications give readers the ability to customize fonts, sizes, and spacings, all of which have been shown to improve the reading experience for readers from different demographics. However, tweaking these text features can be challenging, especially given their interactions on the final look and feel of the text. Our solution is to offer readers preset combinations of font, character, word and line spacing, which we bundle together into reading themes. We identify a recommended set of reading themes through data-driven design iterations with the crowd and experts. We show that after four design iterations, we converge on a set of three COR themes (Compact, Open, and Relaxed) that meet diverse readers’ preferences, when evaluating the reading speeds, comprehension scores, and preferences of hundreds of readers with and without dyslexia, using crowdsourced experiments.

The amount of text people need to read and understand grows daily. Software defaults, designers, or publishers often choose the fonts people read in. However, matching individuals with a faster font could help them cope with information overload. We collaborated with typographers to (1) select eight fonts designed for digital reading to systematically compare their effectiveness and to (2) understand how font and reader characteristics affect reading speed. We collected font preferences, reading speeds, and characteristics from 252 crowdsourced participants in a remote readability study. We use font and reader characteristics to train FontMART, a learning to rank model that automatically orders a set of eight fonts per participant by predicted reading speed. FontMART’s fastest font prediction shows an average increase of 14–25 WPM compared to other font defaults, without hindering comprehension. This encouraging evidence provides motivation for adding our personalized font recommendation to future interactive systems.

In our age of ubiquitous digital displays, adults often read in short, opportunistic interludes. In this context of Interlude Reading , we consider if manipulating font choice can improve adult readers’ reading outcomes. Our studies normalize font size by human perception and use hundreds of crowdsourced participants to provide a foundation for understanding, which fonts people prefer and which fonts make them more effective readers. Participants’ reading speeds (measured in words-per-minute (WPM)) increased by 35% when comparing fastest and slowest fonts without affecting reading comprehension. High WPM variability across fonts suggests that one font does not fit all. We provide font recommendations related to higher reading speed and discuss the need for individuation, allowing digital devices to match their readers’ needs in the moment. We provide recommendations from one of the most significant online reading efforts to date. To complement this, we release our materials and tools with this article.

Building graphical user interfaces for visualization authoring is challenging as one must reconcile the tension between flexible graphics manipulation and procedural visualization generation based on a graphical grammar or declarative languages. To better support designers' workflows and practices, we propose Data Illustrator, a novel visualization framework. In our approach, all visualizations are initially vector graphics; data binding is applied when necessary and only constrains interactive manipulation to that data bound property. The framework augments graphic design tools with new concepts and operators, and describes the structure and generation of a variety of visualizations. Based on the framework, we design and implement a visualization authoring system. The system extends interaction techniques in modern vector design tools for direct manipulation of visualization configurations and parameters. We demonstrate the expressive power of our approach through a variety of examples. A qualitative study shows that designers can use our framework to compose visualizations.

Abstract Event sequence datasets with high event cardinality and long sequences are difficult to visualize and analyze. In particular, it is hard to generate a high level visual summary of paths and volume of flow. Existing approaches of mining and visualizing frequent sequential patterns look promising, but have limitations in terms of scalability, interpretability and utility. We propose CoreFlow, a technique that automatically extracts and visualizes branching patterns in event sequences. CoreFlow constructs a tree by recursively applying a three‐step procedure: rank events, divide sequences into groups, and trim sequences by the chosen event. The resulting tree contains key events as nodes, and links represent aggregated flows between key events. Based on CoreFlow, we have developed an interactive system for event sequence analysis. Our approach can compute branching patterns for millions of events in a few seconds, with improved interpretability of extracted patterns compared to previous work. We also present case studies of using the system in three different domains and discuss success and failure cases of applying CoreFlow to real‐world analytic problems. These case studies call forth future research on metrics and models to evaluate the quality of visual summaries of event sequences.

We describe a social bookmarking service de-signed for a large enterprise. We discuss design principles addressing online identity, privacy, information discovery (including search and pivot browsing), and service extensi-bility based on a web-friendly architectural style. In addi-tion we describe the key design features of our implementa-tion. We provide the results of an eight week field trial of this enterprise social bookmarking service, including a de-scription of user activities, based on log file analysis. We share the results of a user survey focused on the benefits of the service. The feedback from the user trial, comprising survey results, log file analysis and informal communica-tions, is quite positive and suggests several promising en-hancements to the service. Finally, we discuss potential extension and integration of social bookmarking services with other corporate collaborative applications.

Chat Spaces are rich persistent chats that provide light-weight shared workspaces for small to medium-scale group activities. Chat Spaces can accommodate brief, informal interactions (similar to Instant Messaging), and can also support longer-term complex threaded conversations including large numbers of people and shared resources. Our design maps a hierarchical thread representation onto a time-ordered two-column user interface. This mapping allows a user to follow the global dynamics of the entire thread in the chronological column on the left while being able to participate in a selected topical branch in a second column on the right. We also present a dynamic thread map that provides an overview of the entire conversation and supports quick navigation of topical branches in the thread.

Electronic mail has become the most widely-used application for business productivity and communication, yet many people are frustrated with their email. Though email usage has changed, our email clients largely have not. In this paper, we describe a prototype email client developed out of a multi-year iterative design process aimed at providing those who "live in their email" with an improved, integrated email experience. We highlight innovative features and describe the user trials for each version of the prototype with resulting modifications. Finally, we discuss how these studies have recast our understanding of the email "habitat" and user needs.